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Favorite Tabular data is a primary method to store data across multiple industries, including financial, healthcare, manufacturing, and many more. A large number of machine learning (ML) use cases deal with traditional structured or tabular data. For example, a fraud detection use case might be tabular inputs like a customer’s
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Shared by AWS Machine Learning June 5, 2020
Favorite AWS Deep Learning AMI (Ubuntu 18.04) is optimized for deep learning on EC2 Accelerated Computing Instance types, allowing you to scale out to multiple nodes for distributed workloads more efficiently and easily. It has a prebuilt Elastic Fabric Adapter (EFA), Nvidia GPU stack, and many deep learning frameworks (TensorFlow,
Read More Shared by AWS Machine Learning June 4, 2020
Favorite This is a guest post by Sam Bean and Nic Roberts II at StockX. In their own words, “StockX is a Detroit startup company revolutionizing ecommerce with a unique Bid/Ask marketplace—our platform models the New York Stock Exchange and treats goods like sneakers and streetwear as high-value, tradable commodities.
Read More Shared by AWS Machine Learning June 4, 2020
Favorite Today we’re announcing Amazon SageMaker Components for Kubeflow Pipelines. This post shows how to build your first Kubeflow pipeline with Amazon SageMaker components using the Kubeflow Pipelines SDK. Kubeflow is a popular open-source machine learning (ML) toolkit for Kubernetes users who want to build custom ML pipelines. Kubeflow Pipelines is
Read More Shared by AWS Machine Learning June 3, 2020
Favorite AWS DeepComposer is an educational AWS service that teaches generative AI and uses Generative Adversarial Networks (GANs) to transform a melody that you provide into a completely original song. With AWS DeepComposer, you can use one of the pre-trained music genre models (such as Jazz, Rock, Pop, Symphony, or
Read More Shared by AWS Machine Learning June 2, 2020
Favorite You can now use Amazon Textract, a machine learning (ML) service that quickly and easily extracts text and data from forms and tables in scanned documents, for workloads that are subject to Service Organization Control (SOC) compliance and International Organization for Standardization (ISO) compliance. This launch builds upon the
Read More Shared by AWS Machine Learning June 2, 2020
Favorite Amazon Alexa is available in 15 locales and eight languages. To understand and respond in different languages, Alexa needs to learn new grammar rules, and the content that powers Alexa needs to be translated to new languages. Additionally, Alexa needs to learn about country-specific topics, such as new soccer
Read More Shared by AWS Machine Learning June 2, 2020
Favorite The world is becoming smaller as many businesses and organizations expand globally. As businesses expand their reach to wider audiences across different linguistic groups, their need for interoperability with multiple languages increases exponentially. Most of the industry work is manual, slow, and expensive human effort, with many industry verticals
Read More Shared by AWS Machine Learning May 30, 2020
Favorite Preferred Networks (PFN) released the first major version of their open-source hyperparameter optimization (HPO) framework Optuna in January 2020, which has an eager API. This post introduces a method for HPO using Optuna and its reference architecture in Amazon SageMaker. Amazon SageMaker supports various frameworks and interfaces such as TensorFlow,
Read More Shared by AWS Machine Learning May 29, 2020
Favorite At re:Invent 2019, AWS shared the fastest training times on the cloud for two popular machine learning (ML) models: BERT (natural language processing) and Mask-RCNN (object detection). To train BERT in 1 hour, we efficiently scaled out to 2,048 NVIDIA V100 GPUs by improving the underlying infrastructure, network, and ML framework. Today,
Read More Shared by AWS Machine Learning May 28, 2020